With the emergence of more and more applications of Internet-of-Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultra-dense IoT networks, the ultra-densely deployed small base stations (SBSs) will consume a large amount of energy. To reduce the network-wide energy consumption and extend the standby time of IMDs and SBSs, under the proportional computation resource allocation and devices' latency constraints, we jointly perform the device association, computation offloading and resource allocation to minimize the network-wide energy consumption for ultra-dense multi-device and multi-task IoT networks. To further balance the network loads and fully utilize the computation resources, we take account of multi-step computation offloading. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we utilize the hierarchical adaptive search (HAS) algorithm to find its solution. Then, we give the convergence, computation complexity and parallel implementation analyses for such an algorithm. By comparing with other algorithms, we can easily find that such an algorithm can greatly reduce the network-wide energy consumption under devices' latency constraints.

翻译：随着越来越多的因特网电话(IoT)移动设备的应用的出现,移动能源需求与有限电池容量之间的矛盾变得日益突出;此外,在超常的IoT网络中,超密集部署的小型基站将消耗大量能源;为了减少整个网络的能源消耗,延长IMD和SBS的待命时间,在按比例计算资源分配和装置的潜伏限制下,我们联合进行设备关联、计算卸载和资源分配,以最大限度地减少超常多功能和多任务IoT网络的全网络能源消耗;为了进一步平衡网络负荷和充分利用计算资源,我们考虑到多步计算卸载。考虑到最终形成的问题处于非线性和混合内插形式,我们利用等级调整搜索算法找到解决办法。然后,我们提供设备组合、计算复杂性和平行执行分析,以尽量减少超常量多功能多功能和多功能的IoT网络的全网络能源消耗量;为了进一步平衡网络负荷和充分利用计算资源,我们考虑到多步骤的卸载。考虑到最终形成的问题在非线性和混合内插式形式,我们利用等级调整搜索算法来找到解决办法。然后,我们提供这种算法的趋同、计算复杂和平行执行分析可以大大降低这种算算法。我们比较了整个网络的能量装置下的系统。